In a integrative network biology study of Eph-ephrin cell-cell contact initiated signaling, we recently developed novel experimental and computational approaches to characterize the intracellular signaling networks that control the sorting of distinct cell types into separate compartments. This is an important problem, since cell compartmentalization is critical for the development of complex tissues, and is usurped in a variety of disease states. We used data-integration (with NetPhorest and NetworKIN) to computationally reconstruct cell-specific information processing during Eph receptor/ephrin-initiated cell sorting. An interesting innovation was to correlate phosphorylation of a kinase activation loop with phosphorylation of a predicted substrate site, and thus to assign kinases and substrates in an activity-dependent manner. Together, the combination of cell-specific proteomics, large-scale functional analysis and modeling provides a systems-level view of Eph receptor/ephrin- mediated signaling and cell sorting. This work provides the first systematic analysis of cell-specific signaling events induced by contact between two different cell populations. This work therefore serves as a precedent for future investigations into normal and pathologic cell-cell interactions and associated phosphorylation-driven networks. At DTU we are studying this and related systems as a function of time and during genetic and chemical perturbations.

An underlying aim with our research in networks is to establish frameworks that similar to weather prediction models can predict the behavior of cellular and biological systems. Our approach is somewhat similar to forecasting in the sense that we quantify molecular dynamics at the genome and proteome in order to predict changes at the phenotypic level. These studies involved large-scale quantitative analysis of genomic mutations, signaling dynamics (e.g. phosphorylation) as well as cell morphology and other phenotypic markers in combination with machine learning based data integration and network model reconstruction.